Il modo comune per calcolare la somiglianza tra due stringhe in un modo 0% -100% , utilizzato in molte librerie, è misurare quanto (in%) dovresti cambiare la stringa più lunga per trasformarla in quella più corta:
/**
* Calculates the similarity (a number within 0 and 1) between two strings.
*/
public static double similarity(String s1, String s2) {
String longer = s1, shorter = s2;
if (s1.length() < s2.length()) { // longer should always have greater length
longer = s2; shorter = s1;
}
int longerLength = longer.length();
if (longerLength == 0) { return 1.0; /* both strings are zero length */ }
return (longerLength - editDistance(longer, shorter)) / (double) longerLength;
}
// you can use StringUtils.getLevenshteinDistance() as the editDistance() function
// full copy-paste working code is below
Calcolando editDistance():
La editDistance()funzione precedente dovrebbe calcolare la distanza di modifica tra le due stringhe. Esistono diverse implementazioni per questo passaggio, ognuna può adattarsi meglio a uno scenario specifico. Il più comune è l' algoritmo di distanza di Levenshtein e lo useremo nel nostro esempio di seguito (per stringhe molto grandi, è probabile che altri algoritmi funzionino meglio).
Ecco due opzioni per calcolare la distanza di modifica:
Esempio di lavoro:
Guarda la demo online qui.
public class StringSimilarity {
/**
* Calculates the similarity (a number within 0 and 1) between two strings.
*/
public static double similarity(String s1, String s2) {
String longer = s1, shorter = s2;
if (s1.length() < s2.length()) { // longer should always have greater length
longer = s2; shorter = s1;
}
int longerLength = longer.length();
if (longerLength == 0) { return 1.0; /* both strings are zero length */ }
/* // If you have Apache Commons Text, you can use it to calculate the edit distance:
LevenshteinDistance levenshteinDistance = new LevenshteinDistance();
return (longerLength - levenshteinDistance.apply(longer, shorter)) / (double) longerLength; */
return (longerLength - editDistance(longer, shorter)) / (double) longerLength;
}
// Example implementation of the Levenshtein Edit Distance
// See http://rosettacode.org/wiki/Levenshtein_distance#Java
public static int editDistance(String s1, String s2) {
s1 = s1.toLowerCase();
s2 = s2.toLowerCase();
int[] costs = new int[s2.length() + 1];
for (int i = 0; i <= s1.length(); i++) {
int lastValue = i;
for (int j = 0; j <= s2.length(); j++) {
if (i == 0)
costs[j] = j;
else {
if (j > 0) {
int newValue = costs[j - 1];
if (s1.charAt(i - 1) != s2.charAt(j - 1))
newValue = Math.min(Math.min(newValue, lastValue),
costs[j]) + 1;
costs[j - 1] = lastValue;
lastValue = newValue;
}
}
}
if (i > 0)
costs[s2.length()] = lastValue;
}
return costs[s2.length()];
}
public static void printSimilarity(String s, String t) {
System.out.println(String.format(
"%.3f is the similarity between \"%s\" and \"%s\"", similarity(s, t), s, t));
}
public static void main(String[] args) {
printSimilarity("", "");
printSimilarity("1234567890", "1");
printSimilarity("1234567890", "123");
printSimilarity("1234567890", "1234567");
printSimilarity("1234567890", "1234567890");
printSimilarity("1234567890", "1234567980");
printSimilarity("47/2010", "472010");
printSimilarity("47/2010", "472011");
printSimilarity("47/2010", "AB.CDEF");
printSimilarity("47/2010", "4B.CDEFG");
printSimilarity("47/2010", "AB.CDEFG");
printSimilarity("The quick fox jumped", "The fox jumped");
printSimilarity("The quick fox jumped", "The fox");
printSimilarity("kitten", "sitting");
}
}
Produzione:
1.000 is the similarity between "" and ""
0.100 is the similarity between "1234567890" and "1"
0.300 is the similarity between "1234567890" and "123"
0.700 is the similarity between "1234567890" and "1234567"
1.000 is the similarity between "1234567890" and "1234567890"
0.800 is the similarity between "1234567890" and "1234567980"
0.857 is the similarity between "47/2010" and "472010"
0.714 is the similarity between "47/2010" and "472011"
0.000 is the similarity between "47/2010" and "AB.CDEF"
0.125 is the similarity between "47/2010" and "4B.CDEFG"
0.000 is the similarity between "47/2010" and "AB.CDEFG"
0.700 is the similarity between "The quick fox jumped" and "The fox jumped"
0.350 is the similarity between "The quick fox jumped" and "The fox"
0.571 is the similarity between "kitten" and "sitting"